System and method for sending advertising data based on data associated with video data

ABSTRACT

A computer readable medium is disclosed containing computer executable instructions that when executed by a computer perform a method, the method including but not limited to monitoring video data for advertising data keys; correlating the advertising data keys with penetration data for an end user; and selecting advertising data for the end user based on the correlation. A system is disclosed that is useful in performing the method. A data structure embedded in a computer readable medium is disclosed that contains data used by the system and method.

FIELD OF THE DISCLOSURE

The present invention relates to the field of targeted advertising.

BACKGROUND OF THE DISCLOSURE

Targeted advertisements have historically been mailed to large targetedgeographic areas such as a particular city, so that regional advertisersreach only persons who are deemed by the advertiser as most likely to beresponsive to their advertisements.

Advertisements are a component in digital video services, including liveor pre-recorded broadcast television (TV), special or pay-per-viewprogramming, video on demand (VOD), and other content choices availableto subscribers.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an illustrative embodiment of a system for sendingadvertising data and monitoring data sent and received by varioussubscriber devices associated with a subscriber for monitoringadvertising impression quality factors data to estimate penetration forthe advertising data;

FIG. 2 depicts a flow chart for functions performed in anotherillustrative embodiment for delivering advertising data to subscribersin a communication system, such as an IPTV system;

FIG. 3 depicts end user connectivity relationships in an illustrativeembodiment;

FIG. 4 depicts a flow chart for functions performed in anotherillustrative embodiment for monitoring data sent and received by varioussubscriber devices associated with subscribers in a communicationsystem, such as an IPTV system;

FIG. 5 depicts a data structure embedded in a computer readable mediumthat is used by a processor and method for delivering advertising data;

FIG. 6 depicts a data flow diagram for functions performed in anotherillustrative embodiment for monitoring data sent and received by varioussubscriber devices associated with subscribers in a communicationsystem, such as an IPTV system;

FIG. 7 depicts a flow chart for functions performed in anotherillustrative embodiment for delivering advertising data monitoring datasent and received by various subscriber devices associated withsubscribers in a communication system, such as an IPTV system;

FIG. 8 depicts a flow chart for functions performed in anotherillustrative embodiment for delivering advertising data monitoring datasent and received by various subscriber devices associated withsubscribers in a communication system, such as an IPTV system; and

FIG. 9 depicts an illustrative embodiment of a machine for performingfunctions disclosed in an illustrative embodiment.

DETAILED DESCRIPTION

Targeted advertising enables advertisers to target their specific marketbased on the programs that are available. For example, beer companiestarget commercials to be played during football games because theybelieve that their target audience will be drinking beer and watchingfootball at that time. While this may work well for beer companies,other advertisers, with a less predictable target behavior may be in aprecarious position considering their products. An example of this mightbe a situation comedy where the characters go to a particular carmanufacturer's dealership to buy a new car. An illustrative embodimentidentifies within the closed captioning data the car manufacturer's nameand queues a commercial for the car manufacturer during the commercialbreak that was previously downloaded from the advertising managementsystem. This allows companies to place their products within programsand advertise for those products at a time when subscribers are mostlikely to retain their thoughts concerning those products. With thegrowing average number of TVs per household, and the growing placementof products within programs, there is significant need for anintelligent model to deliver advertising. This solution allowsadvertisers to place their commercials in a time slot that allows formaximum retention by TV viewers. It also allows service providers toprofit by charging advertisers for each playback of their content. Withtwo-way communication to the receiver, a service provider can determinehow many times a specific commercial was played and charge theadvertiser a fixed price/playback.

Another illustrative embodiment provides of a content controlapplication that provides two-way communication between a backendmanagement system, a backend server management system to serve contentto the receiver, and commercial content that is flagged with specificmetadata, including but not limited to advertising data keys. Duringoff-peak hours, the content control application communicates with thebackend management system and imports available commercial content. Thiscontent would then be indexed on the content control application toallow for fast local content delivery. Once the content controlapplication has indexed the available commercial content it is ready forthat content to be played. As a program is streamed through the output,the content control application will capture and identify all closedcaptioning data and compare it to the index file. When it finds a match,the content control application will wait until the next commercialbreak to play that content. This will work well in an environment wherecommercial break times are standardized. For example, most prime-timetelevision sitcoms will have a 3-4 minute break and will consist ofseveral 30-60 second commercials.

A computer readable medium is disclosed containing computer executableinstructions that when executed by a computer perform a method, themethod including but not limited to monitoring video data foradvertising data keys; correlating the advertising data keys withpenetration data for an end user; and selecting advertising data for theend user based on the correlation. In another embodiment of the medium,in the method the video data further includes but is not limited toclosed captioning data. In another embodiment of the medium, in themethod the penetration data further includes but is not limited to atleast one data set selected from the group consisting of advertisingforwarding data and advertising discussion data for the end user. Inanother embodiment of the medium, in the method the penetration datafurther includes but is not limited to penetration effectivity databased on impression quality factors for the advertising data forwardedby the end users. In another embodiment of the medium, in the method thecorrelating further includes but is not limited to finding a penetrationdata category that matches one of the advertising data keys. In anotherembodiment of the medium, in the method the closed captioning datafurther includes but is not limited to penetration category data forcorrelating with subscriber activity data for the end user.

In another embodiment, a system is disclosed including but not limitedto a processor in data communication with a computer readable medium;and a computer program embedded in the computer readable mediumincluding but not limited to computer executable instructions forexecution by the processor, the computer program including but notlimited to instructions to monitor video data for advertising data keys,instructions to correlate the advertising data keys with penetrationdata for an end user and instructions to select advertising data for theend user based on the correlation. In another embodiment of the systemmedium, the video data further includes but is not limited to closedcaptioning data. In another embodiment of the system, the penetrationdata further includes but is not limited to at least one data setselected from the group consisting of advertising forwarding data andadvertising discussion data for the end user. In another embodiment ofthe system, the penetration data further includes but is not limited topenetration effectivity data based on impression quality factors for theadvertising data forwarded by the end users. In another embodiment ofthe system, the instructions to correlate further comprise instructionsto find a penetration data category that matches one of the advertisingdata keys. In another embodiment of the system, the closed captioningdata further includes but is not limited to penetration category datafor correlating with subscriber activity data for the end user.

In another embodiment, a computer readable medium is disclosed,containing a data structure for containing data useful in sendingadvertising data the data structure including but not limited to a firstfield for containing data indicative of members of a community of endusers in a data communication system; and a second field for containingdata indicative of advertising penetration data for members of thecommunity of end users. In another embodiment of the medium, the datastructure further includes but is not limited to a third field forcontaining data indicative of advertising penetration effectivity datafor the members of the community of end users. In another embodiment ofthe medium, the data structure further includes but is not limited to afourth field for containing data indicative of advertising data key datafor selecting advertising data from the advertising key data. In anotherembodiment of the medium, the data structure further includes but is notlimited to a fifth field for containing data indicative of advertisingforwarding data. In another embodiment of the medium, the data structurefurther includes but is not limited to a sixth field for containing dataindicative of advertising discussion data. In another embodiment of themedium, the data structure further includes but is not limited to aseventh field for containing data indicative of advertising penetrationcategory data.

In a particular illustrative embodiment, a system and method aredisclosed for estimating penetration effectivity (PE) indices foradvertisements, indicating not only which advertising data items wereforwarded and/or discussed by end users, but also on how many devicesupon which the advertising data were viewed and for how long/whichportions of the advertising data were viewed, by which audiences and theeffect the advertising data had on the recipient audiences. Accurateadvertising ratings can be made available based on correlating programand advertising insertion data stored on video services servers (orembedded in content from video service providers) with subscriberactivity logs which track customers' viewing behavior in some detail.Demographic data on customers also can be correlated with advertisingratings at the aggregate level.

In another particular embodiment, a computer readable medium isdisclosed containing a computer program that when executed by aprocessor performs a method for estimating PE for targeted advertisingdata in a community of end users (also referred to herein as “users” or“subscribers”) on a communication network, the computer programincluding but not limited to instructions to correlate impressionquality factors categories data with a subscriber activity data profilefor purchases and consumption related to an advertising category for thetargeted advertising data; and instructions to estimate from thecorrelation the PE in the advertising category for the targetedadvertising data. In another particular embodiment of the medium theinstructions to estimate the PE further comprise instructions to add areciprocal for a quality of impression for the advertising data tostrength of response (SOR) for the advertising data, wherein the SORindicates a degree of impact on the subscriber in an advertisingcategory for the advertising data.

In another particular embodiment of the medium the impression qualityfactors categories data comprise combinations of impression qualityfactors data from at least two factors selected from the groupconsisting of subscriber device state data indicative of a degree ofactive advertising data viewing, subscriber type data indicative of atype of subscriber device receiving the advertising data, contentcharacter data indicative of a content character and subscriber typedata indicative of a type of subscriber viewing the advertising data.

In another particular embodiment of the medium the impression qualitycategories data are formed by sorting impression quality factors datainto the impression quality factors categories data, applying weights tothe sorted impression quality factors categories data, and accumulatingthe weighted impression quality factors categories data into theimpression quality factors categories data. In another particularembodiment of the medium the subscriber device type is selected from thegroup consisting of a personal computer, a mobile telephone, atelevision monitor, personal data assistant and web tablet. In anotherparticular embodiment, of the method the subscriber type is selectedfrom the group consisting of gender, age, income, geographic location,race and language. In another particular embodiment of the medium thesubscriber device state is selected from the group consisting of speakervolume, display on duration, display off duration and multiple deviceusage, end user device preference, and current device.

In another particular embodiment, a system is disclosed for estimatingPE for targeted advertising data in a community of users in acommunication network, the system including but not limited to aprocessor in data communication with a computer readable medium; and acomputer program embedded in the computer readable medium useful forperforming a method for estimating PE for targeted advertising data in acommunication network, the computer program comprising instructions forcorrelating impression quality factors categories data with a subscriberactivity data profile for purchases and consumption related to anadvertising category for the targeted advertising data and estimatingfrom the correlation the PE in the advertising category for the targetedadvertising data.

In another particular embodiment of the system, the computer program forestimating the PE further includes but is not limited to instructions toadd a reciprocal for a quality of impression for the advertising data toa strength of response for the advertising data, wherein the strength ofresponse indicates a degree of impact on the subscriber in anadvertising category for the advertising data. In another particularembodiment of the system, the computer program further includes but isnot limited to instructions for the estimating the strength of responseby a difference between subscriber purchases in the advertising categorybefore an impression for the advertising data and after the impressionfor the advertising data.

In another particular embodiment of the system the computer programfurther includes but is not limited to instructions to estimate thestrength of response further include but are not limited to instructionsfor dividing the difference by a tendency in the advertising category,wherein the tendency is estimated as the sum of searches by thesubscriber in the advertising category multiplied by a weighting factorM plus purchases by the subscriber in the advertising categorymultiplied by a weighting factor N. In another particular embodiment ofthe system the impression quality factors categories data comprisecombinations of impression quality factors data from at least twofactors selected from the group consisting of subscriber device statedata indicative of a degree of active advertising data viewing,subscriber device type data indicative of a type of subscriber devicereceiving the advertising data, content character data indicative of acontent character and subscriber type data indicative of a type ofsubscriber viewing the advertising data.

In another particular embodiment of the system the impression qualitycategories data are formed by sorting impression quality factors datainto the impression quality factors categories data, applying weights tothe sorted impression quality factors categories data, and accumulatingthe weighted impression quality factors categories data into theimpression quality factors categories data. In another particularembodiment of the system the subscriber device type is selected from thegroup consisting of a personal computer, a mobile telephone, atelevision monitor, personal data assistant and web tablet. In anotherparticular embodiment of the system the subscriber type is selected fromthe group consisting of gender, age, income, geographic location, raceand language.

In another particular embodiment of the system, the subscriber devicestate is selected from the group consisting of speaker volume, displayon duration, display off duration and multiple device usage, end userdevice preference, and current device. In another particular embodimentof the system the content character is selected from the groupconsisting of first run, rerun, special event, series episode andfinale.

In another particular embodiment a data structure embedded in a computerreadable medium is disclosed, the data structure comprising a firstfield for storing data indicative of PE for targeted advertising data inan advertising category based on a correlation between impressionquality factors data and subscriber activity data. In another particularembodiment, the data structure further includes but is not limited to asecond field for storing data indicative of a quality of impression, Qwherein Q is based on the impression quality factors data. In anotherparticular embodiment of the data structure further includes but is notlimited to a third field for storing data indicative of SOR forcontaining data indicative of the SOR based on a difference betweenpresent consumption and past consumption in an advertising category forthe advertising data divided by a sum of searches by the subscriber inthe advertising category multiplied by a weighting factor M plus aconsumption by the subscriber in the advertising category multiplied bya weighting factor N.

In another particular embodiment, a computer readable medium isdisclosed containing computer program instructions that when executed bya computer perform a method for estimating PE for targeted advertisingdata in a communication network, the computer program comprisinginstructions to correlate impression quality factors categories datawith a subscriber activity data profile for purchases and consumptionrelated to an advertising category for the targeted advertising data;and instructions to estimate from the correlation the effectivity indexin the advertising category for the targeted advertising data. Inanother particular embodiment, a client device is disclosed comprising amemory containing a computer program, the computer program furthercomprising instructions to collect impression quality factors categoriesdata comprising combinations of impression quality factors data from atleast two factors selected from the group consisting of subscriberdevice state data indicative of a degree of active advertising dataviewing, subscriber device type data indicative of a type of subscriberdevice receiving the advertising data, content character data indicativeof a content character and subscriber type data indicative of a type ofsubscriber viewing the advertising data.

In another particular embodiment, a system and method distinguishbetween real-time versus time-shifted viewing: Consumers who off-shifttheir viewing by using mechanisms such as DVR and TiVo™ may be motivatedto do this partially by the opportunity it affords to fast-forward overadvertisements during replay. In addition, some pre-recorded broadcastscontaining embedded advertising data are never viewed (estimates rangeas high as one-third); or may be viewed so much later thatadvertisements have lost their value due to stale or expired offers thatare no longer relevant. Another illustrative embodiment provides fortracking viewer ship on increasingly numerous alternative viewingdevices, such as mobile MP3/video players, cell phones, and otherpersonal mobile devices, as well as traditional in-home television sets.

In another particular embodiment, a system and method estimate an“engagement” or depth of experience—how “active” is “activeviewing/listening.” This is gauged by external indicators such aswhether the sound during an advertisement on a subscriber device wastuned low, only the first few or last few seconds of a 30-secondadvertising spot were viewed, by which viewers in particular, and so on.

Another illustrative embodiment provides for monitoring of advertisementviewing by demographically-differentiated audiences. Monitoring can beperformed for advertisements viewed during normal real-timebroadcasting, for both national and local channels; advertisementsviewed when replayed from any pre-recorded broadcasts; andadvertisements included as headers or trailers in video-on-demandplayouts or spliced into streaming media. Exactly which part(s) of theadvertisements were viewed for how long is available with per-second orhigher accuracy. Demographic differentiators can include but are notlimited to viewership by community location and income level brackets,as well as estimates of the number of viewers by age, educational,professional, race, and gender categories, qualified by probability.

Another embodiment correlates records which indicate when and for howlong advertisements occur in any media available for consumption bysubscribers, with records which indicate exactly what the state ofsubscribers' devices is during such designated intervals while the mediais being consumed. For example, suppose a 30-second advertisement occursone minute after the broadcast of a TV series episode starts; thesubscriber has programmed a set top box (STB) to record the givenepisode; and the subscriber plays back the pre-recorded show the nextday. Both the pre-recording and the playback can be dependent on acommunication system provider, such as an Internet protocol television(IPTV) system, which through internal processing, are captured by IPTVlogging. Substantially all media assets referred to herein as contentavailable to subscribers are inventoried with advertisements, either asprovided by the supplying vendor in metadata when uploaded, or asspliced in during broadcast at national or local acquisition servers orat the receiving subscriber device.

During playback on a subscriber device, records indicating subscriberbehavior, including whether or not and for how long audio on asubscriber device has been muted and/or fast forward or other controlshave been hit, are collected and stored in a subscriber activity dataprofile and impression quality factors data. Therefore, correlating thetime during which the advertisement plays back with subscriber behaviorindicates whether the advertisement was rendered to the screen and/orspeakers, which portions, and for how long to per-second or greateraccuracy.

Another illustrative embodiment provides subscriber activity dataprofile records and impression quality factors data. That is, thesesubscriber activity data profile can identify when and for how longadvertisements occur within available media, to identify subscriberbehavior about media content consumption. Another particular embodimentmonitors substantially all subscribers and substantially all subscriberdevices and generates events and records of subscriber activity andimpression quality factors data on a per-device and per-subscriberbasis. There is substantially no limitation to any specific type of STB,or even to STB devices; substantially all consumer devices, such as cellphones or personal data assistant (PDA's) capable of consuming IPTVtriple-play or bundled services (IPTV, voice over internet protocol(VoIP) and Internet), are eligible for monitoring. In another particularembodiment, note that the mechanism introduces no distinctions betweencontent such as national or local broadcast stations, streaming video orreal-time broadcasting, or even between audio, video, and internetconsumption; in that records distinguish advertisements fromnon-advertisements by temporal indicators at sub-second granularity.

In another particular embodiment, advertising data contain digital audioor video markers that are sensed during play back on a subscriber devicethat indicate advertising playback on a subscriber device at 100%, 75%,50% and 25% duration. In particular, viewership of much more than“traditional” advertisements can be tracked using digital audio or videomarkers or temporal indicators or by correlating impression qualityfactors data with timing of advertising data presentation on asubscriber device. For example, VOD headers and trailers, or segmentswhich feature “product placement” can be identified by markers or by atime in which the product placement, header or trailer appears incontent on a subscriber device.

Internet surfing and interactive gaming are monitored, as well forsubscriber activity data and impression quality factors data to estimateadvertising data penetration. The IPTV system monitors subscriber datatransactions, electronic program guides and metadata which distinguishesadvertising data from content. With respect to internet usage, IP-levelaccess records indicate which sites were displayed to the screen and/orplayed on the speaker. Monitoring can be narrowly targeted with respectto collection intervals, audience, and types of devices, as well asrestricted to defined levels of aggregation. With respect to gaming orinteractive media consumption, vendor-specific agreements can provideappropriate metadata and/or algorithms to estimate temporal markers foradvertisements.

Another particular embodiment provides opportunities for correlation ofadvertisement viewership with patterns of consumer behavior. Forexample, tracking viewership of an advertised media event and estimatinga degree of correlation that exists between having viewed itsadvertisement(s) and tuning into, and/or pre-recording, the event.Another embodiment estimates how a degree of correlation differsdepending on whether the advertising data is presented in an episode ina “regular” series, a “special” episode in a regular series, specialevent (SUPER BOWL™, etc.) or a pay-per-view show. Another embodimentcorrelates consumers activating a new IPTV-capable device on their homenetwork with having viewed advertisement(s) for the device.

Another embodiment tracks how many and which subscriber devices are inuse referred to as (multiple device usage), including patterns for whenand how each subscriber device is utilized over time, or when and howthe subscriber devices may be used simultaneously or separately. At thislevel of granularity, estimates about how many viewers and the qualityof the viewing that occurred for specific advertisements, and thedemographics of each viewer, are derived and qualified by degree ofprobability. For example, during installation or troubleshooting,technicians may have recorded the exact placement of subscriber devicesin the home, in relation to which household members were likely to useeach subscriber device, as well as some personal characteristics ofhousehold members.

In particular, any available subscriber-specific information regardingdevice placement and IPTV VoIP and Internet consumption habits can beleveraged, as long as the final results of such calculations are limitedto aggregate quantities not trackable to specific customers. Anotherembodiment provides for demographically rich data mining ofadvertisement viewing correlated with consumer media and productconsumption behavior in a subscriber activity data profile andimpression quality factors data.

Another embodiment records and provides details of which parts ofadvertisements were rendered to the screen and/or played in audio downto per-second granularity. Due to the availability of per-subscriberrecords independently maintained in the IPTV triple play system forpurposes of billing and customer care, correlation of customer behaviorwith demographic factors are calculated, within well-defined categoriesor qualified degrees of probability, at aggregate levels, whilemaintaining proper safeguards for privacy concerns of customers.

A subscriber impression quality factors data profile can be built bycorrelating such subscriber related statistics and the subscriberactivity data profile along with other subscriber data and informationsuch as gender, age, income, languages spoken, areas of interest, etc.volunteered by a subscriber during an IPTV registration process. Inanother particular embodiment the subscriber activity data profileinformation contains data for which a subscriber has opted in formonitoring and use by an IPTV system (providing IPTV, VoIP and Internet)for the purposes of receiving targeted advertising data. Impressionquality factors data can be estimated from data included in theimpression quality factors data, including but not limited to devicetype, subscriber type, and device state based on the subscriber activitydata profile.

Based on subscribers' interests, background, and subscriber profilingresults, one of the following targeted advertising data deliverydescribed herein or an equivalent thereof can be utilized to estimate PEfor targeted advertising data provided to personalized advertising dataand television commercial delivery to IPTV television displays, portablesubscriber data and messaging devices such as mobile or cell phones andwebsite banners and pop up displays on a PC or Laptop.

Turning now to FIG. 1, the IPTV system 100 delivers video data includingbut not limited to content and targeted advertising to subscriber households 113 and associated end user devices (referred to herein assubscriber devices) which may be inside or outside of the household. Thevideo data further includes but is not limited to advertising data keyswhich are embedded in closed captioning data. The advertising data keysinclude but are not limited to text, audio, imagery and video data addedto the closed captioning data for the video. The advertising data keysare generated from an aural recognition and pattern recognition analysisof the video data. Advertisers select particular advertising data keyscategories for detection of advertising opportunities in the video data.When a particular advertising data key category is detected in a videodata stream, an advertising data key is placed in the video data or anassociated data stream such as the closed captioning data streamassociated with the video data.

Television advertising data advertising data keys are inserted by theadvertising server 138. In the IPTV system, IPTV channels are firstbroadcast in an internet protocol (IP) from a server at a super huboffice (SHO) 101 to a regional or local IPTV video hub office (VHO)server 103, to an intermediate office (IO) server 107 and to a centraloffice (CO) 103. The IPTV system 100 includes a hierarchically arrangednetwork of servers wherein a particular embodiment the SHO transmitsvideo and advertising data to a video hub office (VHO) 103 and the VHOtransmits to an end server location close to a subscriber, such as a COserver 103 or IO 107. In another particular embodiment, each of the SHO,VHO, CO and IO are interconnected with an IPTV transport 139. The IPTVtransport 139 may consist of high speed fiber optic cablesinterconnected with routers for transmission of internet protocol data.The IPTV servers also provide data communication for Internet and VoIPservices to subscribers.

Actively viewed IPTV channels are sent in an Internet protocol (IP) datamulticast group to access nodes such as digital subscriber line accessmultiplexer (DSLAM) 109. A multicast for a particular IPTV channel isjoined by the set-top boxes (STBs) at IPTV subscriber homes from theDSLAM. Each SHO, VHO, CO, IO and STB includes a server 115, processor123, a memory 127, network interface 188 and a database 125. Analysis ofthe video data for advertising data key insertion is performed byprocessor 123 at the VHO. The network interface functions to send andreceive data over the IPTV transport. The CO server delivers IPTV,Internet and VoIP content to the subscriber via the IO and DSLAM. Thetelevision content is delivered via multicast and television advertisingdata via unicast or multicast depending on a target televisionadvertising group of end user client subscriber devices to which theadvertising data is directed.

In another particular embodiment, subscriber devices, also referred toherein as users and as end user devices, are different stationary andmobile devices, including but not limited to, wire line phones 135,portable phones 133, lap top computers 118, personal computers (PC) 110and STBs 102, 119 communicate with the communication system, i.e., IPTVsystem through residential gateway (RG) 164 and high speed communicationlines such as IPTV transport 139. In another particular embodiment, DPIdevices 166 inspect data VoIP, Internet data and IPTV video, commandsand Meta data (multicast and unicast) between the subscriber devices andthe IPTV system severs. DPI devices are used in analysis of the videodata for insertion of the advertising data keys based on advertisingdata categories stored in the data base 125. In a particular embodimentadvertising data forwarding and discussion of advertising data anduser-to-user connectivity are detected by the DPI devices that monitorat data sent between end users. End user source and destinationidentifier data in data sent between end users are used to trackuser-to-user connectivity. Image, text and sound recognition functionsare used to detect advertising data discussion and forwarding inaddition to the DPI devices. Textual and aural key words and imageryfound in the advertising data and messages sent and received by end userdevices are inspected by the DPI devices 166 and image recognitionfunctions in the processors 123 in the communication system servers andend user devices are used as indicators found in messages sent betweenusers to estimate penetration of advertising data from discussion andforwarding of the advertising data between users, also referred toherein as end user devices. Impression quality factions for end useridentified in penetration data are used to estimate PE.

In another illustrative embodiment impression quality factors data aremonitored and collected whether or not the subscriber's devices are inthe household 113 or mobile outside of the household such as cellularphones 134. When outside of the household, subscriber mobile device datais monitored by communication network (e.g. IPTV) servers and DPIdevices which associate the impression quality factors data withparticular subscribers. In another particular embodiment, impressionquality factors data including subscriber activity data such ascommunication transactions are inspected by DPI devices located in acommunication system, e.g., IPTV system servers. These communicationsystem servers route the impression quality factors data to a VHO or COin which the impression quality factors data for a subscriber are storedfor processing.

In another particular embodiment, the end user devices or subscriberdevices (“users”) include but are not limited to a client user computer,a personal computer (PC) 110, a tablet PC, a set-top box (STB) 102, aPersonal Digital Assistant (PDA), a cellular telephone 134, a mobiledevice 134, a palmtop computer 134, a laptop computer 110, a desktopcomputer, a communications device, a wireless telephone, a land-linetelephone, a control system, a camera, a scanner, a facsimile machine, aprinter, a pager, a personal trusted device, a web appliance, a networkrouter, switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that machine. In another particular embodiment, a deep packetinspection (DPI) device 124 inspects multicast and unicast data,including but not limited to VoIP data, Internet data and IPTV video,commands and Meta data between the subscriber devices and betweensubscriber devices and the IPTV system severs.

In another illustrative embodiment impression quality factors data aremonitored and collected whether or not the subscriber devices are in thehousehold 113 or the devices are mobile devices 134 outside of thehousehold. When outside of the household, subscriber mobile device datais monitored by communication system (e.g. IPTV) servers which associatethe impression quality factors data with each particular subscriber'sdevice. In another particular embodiment, impression quality factorsdata including subscriber activity data such as communicationtransactions are inspected by DPI devices located in a communicationsystem, e.g., IPTV system servers. These communication system serversroute the impression quality factors data to a VHO in which theimpression quality factors data for a subscriber are stored forprocessing in determining advertising data penetration and compensationfor HCUs based on their contribution to the penetration.

As shown in FIG. 1 advertising sub groups 112 (comprising a group ofsubscriber house holds 113) receive multicast advertising data andadvertising data keys in video data stream from IO server 107 via CO 103and DSLAM 109 at STB 102. Individual households 113 receive advertisingdata at set top box 102 or one of the other subscriber devices. Morethan one STB (see STB1 102 and STB2 119) can be located in an individualhousehold 113 and each individual STB can receive a separate multicastor unicast advertising stream on IPTV transport 139 through DSLAM 109.In another particular illustrative embodiment separate and uniqueadvertising data are displayed at each set top box (STB) 102, 119tailored to target the particular subscriber watching television at thatparticular STB. Each STB 102,119 has an associated remote control (RC)116 and video display 117. The subscriber via the RC selects channelsfor a video data viewing selection (video programs, games, movies, videoon demand) and places orders for products and services over the IPTVsystem 100. Advertising data keys are generated and inserted at the VHOand used to select advertising data that is then sent to end userdevices. In another embodiment, advertising data keys are generated atthe end user devices by processors at the end user devices. Advertisingdata at the end user devices can then be selected for display by the enduser devices based on processing of the advertising data keys describedherein.

FIG. 1 depicts an illustrative communication system, including but notlimited to a television advertising insertion system wherein televisionadvertising data can be inserted at an IPTV (SHO, VHO, CO) server or atthe end user client subscriber device, for example, an STB, mobilephone, web browser or personal computer. Advertising data can beinserted into an IPTV video stream via advertising insertion device 129at the IPTV VHO server 105 or at one of the STBs 102, 109. The IPTVservers include an advertising server 138 and an advertising database139. The advertising data is selected by advertising selection element129 from the advertising database 125 based on a holistic subscriberprofile and delivered by the VHO advertising server 138 to the IPTV VHOserver 115. An SHO 101 distributes data to a regional VHO 103 whichdistributes data to local COs 105 which distribute data via IO 107 to adigital subscriber access line aggregator multiplexer (DSLAM) accessnode to subscriber devices such as STBs 102, 119, PC 110 wire line phone135, mobile phone 133 etc. Advertising data is also selected based onthe community profile for users in the community and sent to a mobilephone or computer associated with the subscriber or end user devices inthe community. The community subscriber profile is built based on acommunity of subscriber's IPTV, Internet and VoIP activity. Compensationsystem 121 determines compensation for HCUs based on advertising datapenetration.

Turning now to FIG. 2, depicts a flow chart for functions performed inanother illustrative embodiment for delivering advertising data tosubscribers in a communication system, such as an IPTV system. As shownin FIG. 2 in block 202 an illustrative embodiment receives data, forexample, closed captioning data containing advertising data keysassociated with video data. In block 204 a particular illustrativeembodiment correlates the advertising data keys with advertising types,penetration data and penetration categories. In block 206, a particularillustrative embodiment selects advertising data to send to the end userbased on the correlation. In block 208 a particular illustrativeembodiment sends the selected advertising data to the end user.

Turning now to FIG. 3, in an illustrative embodiment a community of endusers 302 includes a number of subscribers or users (end user devices)304, 306, 308, 310, 312, 314, 316, and 318. User-to-user connectivity ismeasured by the number, frequency, duration and data volume ofinteractions 303 between end users 304, 306, 308, 310, 312, 314, 316,and 318. As shown in FIG. 3, end users 310, 312, 314, 316, and 318 havesubstantially more user-to-user connectivity than end users 304, 306 and308. In a particular embodiment, end users 310, 312, 314, 316, and 318are classified as HCUs. Each HCU has a ZOI defined by connectivitybetween the HCU and end users outside of the community 302. The ZOI forHCU 312 includes end users 316, 318 and 320. HCU to ZOI connectivity ismeasured by the number, frequency, duration and data volume ofinteractions 305 between the HCU and the end users 316, 318 and 320 inthe ZOI. HCU 312 connectivity outside of the ZOI is measured by thenumber, frequency, duration and data volume of interactions 307 betweenthe HCU 312 ZOI end users 316, 318 and 320 and end users 328, 330, 332and 334. Interactions 303, 305 and 307 are monitored to determineadvertising data forwarding, advertising data discussion and advertisingdata penetration within the ZOI and beyond the ZOI.

Turning now to FIG. 4, in a particular embodiment, a flow chart 400 offunctions are performed. The order of execution of functions and theselected functions to be executed is different in different embodimentsand is not limited to the order of execution and functions shown in FIG.4. As shown in FIG. 4, in block 402 an illustrative embodiment measuresuser-to-user connectivity between end users within the community ofusers in a communication system. In block 404 an illustrative embodimentdetermines HCUs as users who are most connected to others end users inthe community. In block 406 an illustrative embodiment determines a ZOIfor each HCU. In block 408 an illustrative embodiment characterizes eachHCU ZOI by type of affiliation (i.e., type of data exchange anddiscussion type, i.e. sports, news, fusion etc.) between end users inthe HCU ZOI. In block 410 an illustrative embodiment assigns weights toeach HCU according to the HCU ZOI characterization for the HCU. The HCUZOI characterization includes but is not limited to type of affiliation,frequency and duration of connections to ZOI and beyond the ZOI.

In block 412 an illustrative embodiment determines a device type for anHCU and end users in the community and ZOI used to connect to each otherfor advertising data forwarding and advertising data discussion. Inblock 414 an illustrative embodiment a VHO sends advertising data to anuppermost top tier HCU in the community based on ZOI characterization.In block 416, an illustrative embodiment measures interactions includingbut not limited to advertising data forwarding and advertising datadiscussion in each HCU in the community of end users and between the HCUand the end users in the HCU ZOI. In block 418 an illustrativeembodiment measures or determines penetration of advertising data fromeach HCU and the community in which the HCUs reside based on forwardingof advertising data and discussion of advertising data from the HCU tothe community and to the end users in the ZOI. In block 420 anillustrative embodiment determines compensation for each HCU based onpenetration of the advertising data within each HCU ZOI from each HCU.In a particular embodiment the community is a group of end usersassociated by similar demographic, geographic or interests.

Turning now to FIG. 5, in a particular illustrative embodiment a datastructure embedded in a computer readable medium is disclosed. The datastructure includes but is not limited to a first field 502 forcontaining data indicative of community member data identifying endusers in the community of end users in a communication system. In aparticular embodiment, the data structure further includes a secondfield 504 for containing data indicative of HCUs in the community of endusers. In a particular embodiment, the data structure further includes athird field 506 for containing data indicative of a ZOI of end users inan HCU ZOI for each HCU identified in field 504. In a particularembodiment, the data structure further includes a fourth field 508 forcontaining data indicative of ZOI characterization for each HCU ZOI. Ina particular embodiment, the data structure further includes a fifthfield 510 for containing data indicative of HCU weights for each HCU. Ina particular embodiment, the data structure further includes a sixthfield 512 for containing data indicative of containing correlation dataindicating a correlation between advertising characterization data andthe HCU weights. In a particular embodiment, the data structure furtherincludes a seventh field 514 for containing data indicative of advertingdata sent to the HCUs and monitored for forwarding, discussion,penetration and PE.

In a particular embodiment, the data structure further includes aneighth field 516 for containing data indicative of advertising dataforwarding from the HCU and from the ZOI. In a particular embodiment,the data structure further includes a ninth field 518 for containingdata indicative of advertising discussion between the HCU and members ofthe community and between the community and the HCU ZOI which startedfrom the HCU. In a particular embodiment, sender identifiers in the datatransmissions are used to identify, in an IPTV server or DPI device, thesource of a forwarded advertising data and discussion message. In aparticular embodiment, the data structure further includes a tenth field520 for containing data indicative of advertising penetration data forthe identified advertising data 514. In a particular embodiment, thedata structure further includes an eleventh field 522 for containingdata indicative of advertising penetration effectivity data. In aparticular embodiment, the data structure further includes a twelfthfield 524 for containing data indicative of advertising key data. In aparticular embodiment, the data structure further includes a twelfthfield 526 for containing data indicative of advertising category data.In a particular embodiment, the data structure further includes athirteenth field 528 for containing data indicative of advertisingpenetration category data.

FIG. 6 depicts a data flow diagram for another illustrative embodimentof a system for sending advertising data and monitoring data sent andreceived by various subscriber devices associated with subscribers in anIPTV system 100 for monitoring advertising impression quality factorsdata, advertising data forwarding and advertising data discussion forthe subscriber devices. In a particular, illustrative embodiment, theimpression quality factors data 602 are accumulated at a subscriberdevice or through database entries available in the IPTV networksubscriber devices report their impression quality factors data to theIPTV system. As shown in FIG. 6 the device state 610, device type 616and subscriber type 612 are accumulated as impression quality factorsdata 602. These impression factors quality data, including but notlimited to advertising data discussion and advertising data forwarding,are categorized into impression quality factors data categories, andweighted at 604 using weights assigned by the IPTV system for particularimpression factor quality data categories. The weighted HCUs, impressionquality factors and categories data are correlated with the subscriberactivity data 614. The correlation of the weighted, impression qualityfactors categories data and the subscriber activity data are utilized toestimate the penetration 608 for the advertising data.

Turning now to FIG. 7, in an illustrative embodiment a function 700 isperformed to correlate the impression quality factors category data withthe subscriber activity data. The subscriber activity data includes datafrom a subscriber activity data profile which chronicles purchases andmedia consumption for a subscriber. Purchases can include but are notlimited to purchases over the Internet via eCommerce as well aspurchases of media content such as music, movies, books and video ondemand. Media consumption can include but is not limited to programswatched, web sites visited, games played, searches performed and musicdownloaded. Subscriber activity data is collected at the subscriberdevice and at the IPTV system though monitoring data sent and receivedto and from the subscriber devices. Subscriber activity data includesbut is not limited to data indicating advertising data forwarding,discussion and user-to-user connectivity for a subscriber. As shown atblock 702, a particular embodiment estimates the quality of advertisingimpression, Q using the impression quality factors categories data. Theimpression quality factors data are sorted into categories and weightedas discussed below.

At block 704 a particular embodiment estimates the strength of response(SOR). The SOR is a measure of the impact or degree of influence that aparticular advertising data has on a subscriber in a particularadvertising category, based on changes in the subscriber's purchasingand/or consumption after receiving the advertising data directly,forwarded from another user or discussed with another user. The rate ofchange over time for an SOR in a particular advertising category is atrend for the particular advertising category. The advertising categorymay be associated with or the same as one of the impression qualityfactors categories. The subscriber's purchasing and/or consumption trendis estimated from changes in the subscriber's subscriber activity dataprofile in a particular advertising category. The subscriber activitydata profile captures purchases and/or consumption by a subscriber bytracking transactions and selections made on the IPTV triple playnetwork and sorting the transactions into advertising, product andinterest categories. These purchases and consumption may include but arenot limited to IPTV, VoIP and Internet purchases and consumption. Inanother embodiment, the SOR equals a quantity for present purchasesand/or consumption in a particular advertising category associated withthe advertising data, minus a quantity for past purchases and/orconsumption in a particular advertising category associated with theadvertising data; divided by an indication of the subscribers interestin the advertising category as indicated by a number of searches in theparticular advertising data category times a weighting factor, M pluspurchases and/or consumption in the particular advertising data categorymultiplied by waiting factor, N.

The weighting factors M and N are programmable so that searches in aparticular advertising category can be weighted more or less thanpurchases and/or consumption in a particular advertising category.Advertising categories can include but are not limited to sports,fashion, art, literature, action movies, mysteries, food, travel andhealth. At block 706 a particular embodiment estimates the effectivityindex, (EI) as equal to one divided by the estimate of the quality ofadvertising impression, Q added to the strength of response (SOR). Inanother particular illustrated embodiment, a subscriber household 113sends impression quality factors data from an RG or STB in a subscriberhousehold or from a mobile device to an access node such as a DSLAM 109.When sent to the VHO, the identity of the subscriber is associated withthe impression quality factors data. The identity of the subscriber canbe stripped off of the data as it is aggregated in the IPTV system. Theaccess node 109 sends data to a VHO through a CO.

In another particular illustrative embodiment the service applicationsare provided by a communication network such as an IPTV system. Theservice applications include but are not limited to an IPTV systemproviding IPTV, Internet and VoIP (herein referred to as an IPTVsystem). Advertisements are inserted by the IPTV system into SMSmessages, video and HTML data the IPTV system by advertising server 138.The service VHO communicates with the subscriber household 113 via theIPTV system servers and collects the subscriber data comprising theimpression quality factors data from the household, the access node,aggregation network, service network and service applications.

In another particular illustrative embodiment access node controlprotocol (ANCP) is used to communicate between the service CO or IO inthe communication network and an access node 109. In another particularillustrative embodiment access node 109 is a DSLAM. In anotherillustrative embodiment, the aggregation network or central office 109communicates with the SHO and VHO. In another particular illustrativeembodiment, the CO communicates with the service application or IPTVsystem over an IPTV system communication path.

In another particular illustrative embodiment, the VHO receivesimpression quality factors data, including but not limited to devicestate data indicative of a degree of active advertising data viewing,device type data indicative of a type of advertising device, receivingthe advertising data, and subscriber type data indicative of a type ofsubscriber viewing the advertising data. The impression quality factorsdata further includes but is not limited to channel viewer ship dataincluding but not limited to multicast join data indicating what IPTVprogram a subscriber is watching, subscriber device state data andsubscriber activity data collected from the access node. The VHOreceives the impression quality factors data, advertising dataforwarding data, advertising data discussion data, penetration data andsends the data to the data base 125. The data base 125 collectsimpression quality factors data, including but not limited toadvertising data forwarding and advertising data discussion, appliesweights and curves 130, correlates the weighted and accumulatedimpression quality factors categories data 128 with advertising qualitycriteria data to generate the qualified impression quality count 136.HCUs are compensated based on their contribution to penetration of theadvertising data.

As shown in FIG. 1 impression quality factors data and impressionquality factors categories data 128, impression quality factorcategories weights, subscriber activity data profiles and curves 130 andpenetration data 129 are stored at the VHO data base. The impressionquality factors categories data and subscriber activity data arecorrelated 132 at the CO level and above. A number of customers viewingthe commercial or advertising data vary at each time in a time interval.

Turning now to FIG. 8 a flowchart 800 is illustrated for anotherparticular embodiment in which functions are performed. As shown inblock 802 in another particular illustrative embodiment a functionstarts and proceeds to block 804, where a server receives impressionquality factors data from subscriber devices comprising subscriberdevice state, device type, subscriber type, multiple device usage,current device state, and application curve. In another particularillustrative embodiment at block 806, the impression quality factorsdata are sorted into impression quality factors categories data andweights are applied to the impression quality factors category data toestimate qualified impression quality, Q. At block 808, anotherembodiment accumulates weighted impression quality factors, andgenerates a histogram of the accumulated impression quality factorcategories data. The impression quality factors are applied to thetransactions between end users including but not limited to advertisingdata discussion and advertising data forwarding.

In another particular illustrative embodiment, an advertiser assignsweights from 1-10 to impression quality factor data categories. Thuswhen advertising data is discussed or forwarded to a user, impressionquality factors data are calculated for the recipient of the advertisingdata forwarded or discussion data pertaining to the advertising data.The impression quality factor data categories include but are notlimited to impressions for particular subscriber device types forparticular subscriber types in particular advertising categories.Advertising categories are assigned by the IPTV system. The advertisingcategories in another particular embodiment include but are not limitedto luxury cars, travel, health, education and entertainment. Forexample, impressions are qualified for a forwarded or discussedadvertising data for a particular advertising category, luxury cars fora particular subscriber type, women. In this example, a particularluxury car slanted toward women are assigned weights as follows: Foradvertisements forwarded or discussed and viewed on television, a weightof 10 is assigned for women age 35-55, a weight of 7 for women age18-35, a weight of 8 for men 35-55, a weight of 5 for men 18-35. Foradvertisements viewed on mobile telephones, a weight of 8 for women age35-55, a weight of 5 for women age 18-35, a weight of 6 for men age35-55, a weight of 4 for men age 18-35. For online commercials forwardedor discussed and viewed a weight of 6 for women age 35-55, a weight of 3for women age 18-35, a weight of 4 for men age 35-55, a weight of 2 formen age 18-35.

Geographic weights are also assigned by advertisers based on desiredgeographic coverage for a ZOI and penetration desired for particularadvertising data. A histogram of viewers sorted by impression qualityfactor categories is generated showing how many viewers in eachimpression quality factor category viewed a particular advertisement.Different weights are assigned for advertising data forwarded andadvertising data discussed, depending on the advertiser's set parametersfor calculating advertising data penetration. In a particularembodiment, a weight of 5 out of 10 is assigned for forwardingadvertising data, a weight of 7 assigned for replaying the forwardedadvertising data and a weight of 3 assigned for discussing theadvertising data.

An additional weight point can be assigned (i.e., given a weight of 9instead of 8) to subscribers who receive and view forwarded advertisingdata on a subscriber device that is received and viewed on theirpreferred subscriber device as indicated by a subscriber devicepreference. A subscriber device preference is indicated by a subscriberprofile showing that prior reception of advertisements on a particularsubscriber device type are viewed and not skipped. For example, if asubscriber receives an advertisement on a television for a particularproduct but only views 10 seconds of a 30 second advertisement, butviews the entire advertisement of the same advertisement on a mobilephone, then the subscriber's preferred subscriber device is a mobilephone and advertisements viewed on the mobile phone are given extraweight. In this case the subscriber device preference is the mobilephone. In another particular embodiment, a subscriber device preferenceis indicated by a subscriber selection at registration with acommunication network.

Values can also be assigned for duration or how much of an advertisementa particular subscriber watched. If a subscriber only saw the first 10seconds of a 30 second advertisement, the advertisement viewing receivesa only one sixth of its assigned weight and may be deemed asinappropriate for the demographic and device type for that particularviewer type, for example, males 18-35 on a mobile phone. If the sameadvertisement is watched for the last 20 seconds of the advertisement,the advertisement viewing receives three fourths of its assigned weightand deemed appropriate for the demographic and device type for thatparticular viewer type, for example, males 18-35 on a mobile phone. Theweighted impression quality factors are adjusted for duration andaccumulated for additional processing.

At block 810 a particular illustrative embodiment applies curves to atleast two of the accumulated compression quality factor categories datato generate curve-adjusted impression quality factors categories data.In a particular embodiment, different curves are applied to differentimpression quality factor categories data to generate curve-adjustedimpression quality factor categories data. For example, continuing withthe luxury car example from above, different curves are applied todifferent accumulated impression quality factors categories data. An Scurve in applied data for men ages 18-35 and 35-55, a linear curve todata for women age 35-55 and an exponential curve to data for women age35-55. In another particular illustrative embodiment, at block 812 aparticular illustrative embodiment correlates the curve-adjustedimpression quality factor categories data with a set of advertisingadvertiser quality criteria data to refine the estimate of the qualifiedimpression, Q. The advertiser quality criteria data may favor or weightparticular groups in particular advertising categories at particulartimes and contexts.

The curve adjusted impression quality factors categories generated inblock 810, are compared to advertiser quality criteria data as follows.An advertiser provides impression quality criteria data for ratingimpression quality, Q by device type and subscriber type. In aparticular illustrative embodiment, impression quality criteria datagive a value of 10 points each to every television impression viewed bya woman age 35-55 with an income over $100,000, 9 points for man age35-55 with an income over $100,000 and 8 points for woman age 35-55 withan income $50,000-$99,000. At block 814 a particular embodimentcompensates each HCUs based on advertising data penetration for eachHCU. In a particular illustrative embodiment, the flow of functionexecution ends at block 816.

FIG. 9 is a diagrammatic representation of a machine in the form of acomputer system 900 within which a set of instructions, when executed,may cause the machine to perform any one or more of the methodologiesdiscussed herein. In some embodiments, the machine operates as astandalone device. In some embodiments, the machine may be connected(e.g., using a network) to other machines. In a networked deployment,the machine may operate in the capacity of a server or a client usermachine in server-client user network environment, or as a peer machinein a peer-to-peer (or distributed) network environment. The machine maycomprise a server computer, a client user computer, a personal computer(PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant(PDA), a cellular telephone, a mobile device, a palmtop computer, alaptop computer, a desktop computer, a communications device, a wirelesstelephone, a land-line telephone, a control system, a camera, a scanner,a facsimile machine, a printer, a pager, a personal trusted device, aweb appliance, a network router, switch or bridge, or any machinecapable of executing a set of instructions (sequential or otherwise)that specify actions to be taken by that machine.

It will be understood that a device of the present invention includesbroadly any electronic device that provides voice, video or datacommunication. Further, while a single machine is illustrated, the term“machine” shall also be taken to include any collection of machines thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein.

The computer system 900 may include a processor 902 (e.g., a centralprocessing unit (CPU), a graphics processing unit (GPU), or both), amain memory 904 and a static memory 906, which communicate with eachother via a bus 908. The computer system 900 may further include a videodisplay unit 910 (e.g., liquid crystals display (LCD), a flat panel, asolid state display, or a cathode ray tube (CRT)). The computer system900 may include an input device 912 (e.g., a keyboard), a cursor controldevice 914 (e.g., a mouse), a disk drive unit 916, a signal generationdevice 918 (e.g., a speaker or remote control) and a network interface.

The disk drive unit 916 may include a machine-readable medium 922 onwhich is stored one or more sets of instructions (e.g., software 924)embodying any one or more of the methodologies or functions describedherein, including those methods illustrated in herein above. Theinstructions 924 may also reside, completely or at least partially,within the main memory 904, the static memory 906, and/or within theprocessor 902 during execution thereof by the computer system 900. Themain memory 904 and the processor 902 also may constitutemachine-readable media. Dedicated hardware implementations including,but not limited to, application specific integrated circuits,programmable logic arrays and other hardware devices can likewise beconstructed to implement the methods described herein. Applications thatmay include the apparatus and systems of various embodiments broadlyinclude a variety of electronic and computer systems. Some embodimentsimplement functions in two or more specific interconnected hardwaremodules or devices with related control and data signals communicatedbetween and through the modules, or as portions of anapplication-specific integrated circuit. Thus, the example system isapplicable to software, firmware, and hardware implementations.

In accordance with various embodiments of the present invention, themethods described herein are intended for operation as software programsrunning on a computer processor. Furthermore, software implementationscan include, but not limited to, distributed processing orcomponent/object distributed processing, parallel processing, or virtualmachine processing can also be constructed to implement the methodsdescribed herein.

The present invention contemplates a machine readable medium containinginstructions 924, or that which receives and executes instructions 924from a propagated signal so that a device connected to a networkenvironment 926 can send or receive voice, video or data, and tocommunicate over the network 926 using the instructions 924. Theinstructions 924 may further be transmitted or received over a network926 via the network interface device 920. The machine readable mediummay also contain a data structure for containing data useful inproviding a functional relationship between the data and a machine orcomputer in an illustrative embodiment of the disclosed system andmethod.

While the machine-readable medium 922 is shown in an example embodimentto be a single medium, the term “machine-readable medium” should betaken to include a single medium or multiple media (e.g., a centralizedor distributed database, and/or associated caches and servers) thatstore the one or more sets of instructions. The term “machine-readablemedium” shall also be taken to include any medium that is capable ofstoring, encoding or carrying a set of instructions for execution by themachine and that cause the machine to perform any one or more of themethodologies of the present invention. The term “machine-readablemedium” shall accordingly be taken to include, but not be limited to:solid-state memories such as a memory card or other package that housesone or more read-only (non-volatile) memories, random access memories,or other re-writable (volatile) memories; magneto-optical or opticalmedium such as a disk or tape; and carrier wave signals such as a signalembodying computer instructions in a transmission medium; and/or adigital file attachment to e-mail or other self-contained informationarchive or set of archives is considered a distribution mediumequivalent to a tangible storage medium. Accordingly, the invention isconsidered to include any one or more of a machine-readable medium or adistribution medium, as listed herein and including art-recognizedequivalents and successor media, in which the software implementationsherein are stored.

Although the present specification describes components and functionsimplemented in the embodiments with reference to particular standardsand protocols, the invention is not limited to such standards andprotocols. Each of the standards for Internet and other packet switchednetwork transmission (e.g., TCP/IP, UDP/IP, HTML, and HTTP) representexamples of the state of the art. Such standards are periodicallysuperseded by faster or more efficient equivalents having essentiallythe same functions. Accordingly, replacement standards and protocolshaving the same functions are considered equivalents.

The illustrations of embodiments described herein are intended toprovide a general understanding of the structure of various embodiments,and they are not intended to serve as a complete description of all theelements and features of apparatus and systems that might make use ofthe structures described herein. Many other embodiments will be apparentto those of skill in the art upon reviewing the above description. Otherembodiments may be utilized and derived there from, such that structuraland logical substitutions and changes may be made without departing fromthe scope of this disclosure. Figures are also merely representationaland may not be drawn to scale. Certain proportions thereof may beexaggerated, while others may be minimized. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

The Abstract of the Disclosure is provided to comply with 37 C.F.R.§1.72(b), requiring an abstract that will allow the reader to quicklyascertain the nature of the technical disclosure. It is submitted withthe understanding that it will not be used to interpret or limit thescope or meaning of the claims. In addition, in the foregoing DetailedDescription, it can be seen that various features are grouped togetherin a single embodiment for the purpose of streamlining the disclosure.This method of disclosure is not to be interpreted as reflecting anintention that the claimed embodiments require more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive subject matter lies in less than all features of asingle disclosed embodiment. Thus the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separately claimed subject matter.

1. A computer readable medium containing computer executableinstructions that when executed by a computer perform a method, themethod comprising: monitoring video data for advertising data keys;correlating the advertising data keys with penetration data for an enduser; and selecting advertising data for the end user based on thecorrelation.
 2. The method of claim 1, wherein the video data furthercomprises closed captioning data.
 3. The method of claim 2, wherein thepenetration data further comprises at least one data set selected fromthe group consisting of advertising forwarding data and advertisingdiscussion data for the end user.
 4. The method of claim 2, wherein thepenetration data further comprises penetration effectivity data based onimpression quality factors for the advertising data forwarded by the endusers.
 5. The method of claim 4, wherein the correlating furthercomprises finding a penetration data category that matches one of theadvertising data keys.
 6. The method of 2, wherein the closed captioningdata further comprises penetration category data for correlating withsubscriber activity data for the end user.
 7. A system comprising: aprocessor in data communication with a computer readable medium; and acomputer program embedded in the computer readable medium comprisingcomputer executable instructions for execution by the processor, thecomputer program comprising instructions to monitor video data foradvertising data keys, instructions to correlate the advertising datakeys with penetration data for an end user and instructions to selectadvertising data for the end user based on the correlation.
 8. Thesystem of claim 7, wherein the video data further comprises closedcaptioning data.
 9. The method of claim 8, wherein the penetration datafurther comprises at least one data set selected from the groupconsisting of advertising forwarding data and advertising discussiondata for the end user.
 10. The method of claim 2, wherein thepenetration data further comprises penetration effectivity data based onimpression quality factors for the advertising data forwarded by the endusers.
 11. The system of claim 10, wherein the instructions to correlatefurther comprise instructions to find a penetration data category thatmatches one of the advertising data keys.
 12. The system of 8, whereinthe closed captioning data further comprises penetration category datafor correlating with subscriber activity data for the end user.
 13. Acomputer readable medium, containing a data structure for containingdata useful in sending advertising data the data structure comprising: afirst field for containing data indicative of members of a community ofend users in a data communication system; and a second field forcontaining data indicative of advertising penetration data for membersof the community of end users.
 14. The medium of claim 13, the datastructure further comprising a third field for containing dataindicative of advertising penetration effectivity data for the membersof the community of end users.
 15. The medium of claim 14, the datastructure further comprising a fourth field for containing dataindicative of advertising data key data for selecting advertising datafrom the advertising key data.
 16. The medium of claim 15, the datastructure further comprising a fifth field for containing dataindicative of advertising forwarding data.
 17. The medium of claim 16,the data structure further comprising a sixth field for containing dataindicative of advertising discussion data.
 18. The medium of claim 17,the data structure further comprising a seventh field for containingdata indicative of advertising penetration category data.